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Multi-Scale Feature Fusion and Advanced Representation Learning for Multi label Image Classification

Computers, Materials & Continua 2025年第3期82卷 5285-5306页

作者： Naikang Zhong Xiao Lin Wen Du Jin Shi Institute of Artificial Intelligence on Education Research College of InformationMechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234China Lab for Educational Big Data and Policymaking Ministry of EducationShanghai Normal UniversityShanghai200234China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal UniversityShanghai200234China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234China DS Information Technology Co. Ltd.Shanghai200032China Faculty of Innovation Engineering Macao university of Science and TechnologyMacao999078China

Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in *** class-specific precise representations at different scales is a key aspect of feature ***,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same *** some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different *** address these issues,we propose a two-stage,three-branch Transformer-based *** first stage incorporates multi-scale image feature extraction and hierarchical scale *** design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object *** second stage includes a global feature enhancement module and a region selection *** global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and ***,these components enable the efficient acquisition of class-specific precise feature *** experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed *** approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,*** results validate the reliability and superiority of our approach for multi-label image classification.

关键词： Image classification multi-label multi scale attention mechanisms feature fusion

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Mid-infrared computational temporal ghost imaging

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Light(science & Applications) 2024年第6期13卷 1088-1097页

作者： Han Wu Bo Hu Lu Chen Fei Peng Zinan Wang Goery Genty Houkun Liang College of Electronics and Information Engineering Sichuan UniversityChengduSichuan610064China College of Electrical Engineering Sichuan UniversityChengduSichuan610064China Key Lab of Optical Fiber Sensing&Communications University of Electronic Science&Technology of ChinaChengduSichuan611731China Laboratory of Photonics Tampere UniversityFI-33014TampereFinland

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow *** technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal ***,the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns,which are not available in all spectral regions especially in the mid-infrared ***,we demonstrate a frequency downconversion temporal ghost imaging scheme that enables to extend the operation regime to arbitrary wavelengths regions where fast modulators and detectors are not *** approach modulates a signal with temporal intensity patterns in the near-infrared and transfers the patterns to an idler via difference-frequency generation in a nonlinear crystal at a wavelength where the temporal object can be *** a proof-of-concept,we demonstrate computational temporal ghost imaging in the mid-infrared with operating wavelength that can be tuned from 3.2 to 4.3μ*** scheme is flexible and can be extended to other *** results introduce new possibilities for scan-free pump-probe imaging and the study of ultrafast dynamics in spectral regions where ultrafast modulation or detection is challenging such as the mid-infrared and THz regions.

关键词： ghost Ghost scheme

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A Novel Cryptocurrency Prediction Method Using Optimum CNN

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Computers, Materials & Continua 2022年第4期71卷 1051-1063页

作者： Syed H.Hasan Syeda Huyam Hasan Mohammed Salih Ahmed Syed Hamid Hasan Iowa State University AmesUSA University of Texas at Arlington USA Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

In recent years,cryptocurrency has become gradually more significant in economic regions *** cryptocurrencies,records are stored using a cryptographic *** main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social *** is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media ***,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet *** paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and *** results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

关键词： Cryptocurrency litecoin monero bitcoin ethereum twitter optimal CNN price prediction sentiment analysis

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Facial Expression Recognition in the Wild using Artificial Rabbits Optimizer based Residual Neural Network

Facial Expression Recognition in the Wild using Artificial R...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Metkewar, P.S. Uma, N. Dhanaraj, Rajesh Kumar Prabhu Kavin, Balasubramanian Sathyamoorthy, Malathy Maharashtra Pune India New Horizon College of Engineering Bengaluru560103 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur603203 India Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350344387

In affective computing, emotion acknowledgement in the wild is a much-studied area. Although there have been advancements, the difficulty of emotion acknowledgement in the wild due to head movement, face deformation, illumination fluctuation, etc. remains an open subject. To improve the model's simplification ability and, by extension, its performance across a variety of learning tasks, it is crucial that a wide variety of features be extracted by the deep neural network. Interest in facial expression detection in natural settings has grown in recent years despite the difficulty of obtaining discriminative and informative characteristics from partially obscured photos. In the first stage of this paper's pre-processing, the noisy pictures are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE). After that, Residual Neural Network (ResNet) features extraction is used, and the same method serves as an emotion classifier. An iterative technique called stochastic gradient descent (SGD) is utilized to refine the ResNet model's objective function. Finally, the Artificial Rabbits Optimization Algorithm (ARO) is used to choose the best value for, thereby enhancing the reliability of the categorization. The suggested method is effective, as evidenced by experimental findings on three popular facial expression recognition in-the-wild datasets: AffectNet, AFEW Dataset, and RAF-DB, where it accomplishes state-of-the-art presentation with 96% accuracy and improves upon existing models by roughly 5% to 8%. © 2023 IEEE.

关键词： Artificial Rabbits Optimization Contrast Limited Adaptive Histogram Equalization Emotion recognition Residual Neural Network Stochastic gradient descent

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Ensemble Text Classification with TF-IDF Vectorization for Hate Speech Detection in Social Media

Ensemble Text Classification with TF-IDF Vectorization for H...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sathishkumar, R. Karthikeyan, T. Praveen, Kumar P Shamsundar, S.M. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Sri Manakula Vinayagar Engineering College Department of Computer Science and Business Systems Puducherry India Sri Manakula Vinayagar Engineering College Department of Information Technology Puducherry India

ISBN: (纸本)9798350315127

The development of artificial intelligence (AI) has changed how hate speech is detected. In hate speech identification using machine learning, a number of methods are used to automatically find text that uses vocabulary that is considered to be derogatory, discriminatory, or motivated by hatred. Supervised learning techniques like neural networks, decision trees, and SVMs need a labelled dataset comprising samples of hate speech and non-hate speech. This project investigates the use of AI and machine learning techniques to automatically detect material that uses offensive, intolerant, or hostile words. A voting classifier and TF-IDF representations are combined to improve classification accuracy. The ensemble of classifiers, powered by AI approaches, shows impressive accuracy in identifying hate speech by training five different classifiers (Random Forest, Bagging, Support Vector Machine, AdaBoost, and Gradient Boosting) on a labelled dataset of tweets. The TF-IDF representation prioritises textual terms, whereas the ensemble method uses classifier diversity to capture distinctive patterns. Results from experiments show the strategy's effectiveness, with precision 0.95, recall 0.96, f1-score 0.95 and accuracy 0.97 for detecting hate speech. By successfully utilising AI's capacity to fight hate speech, this research helps the development of a diverse and secure online environment. The suggested approach works well for automatically identifying hate speech, making the internet a safer and more welcoming place for all users. © 2023 IEEE.

关键词： Stochastic systems

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Semiconductor Optical Amplifier based on Optical Reservoir (SOA-OR) for Mode-division Multiplexing Signal Compensation

Semiconductor Optical Amplifier based on Optical Reservoir (...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Luo, Huiwen Liu, Xiyong Zhao, Tianfeng Qiu, Kun Wen, Feng University of Electronic Science and Technology of China Key Lab of Optical Fiber Sensing and Communications Ministry of Education School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350375909

The mode-division multiplexing (MDM) technology is a promising candidate to significantly increase the system capacity of the optical fiber communication networks. However, the more complex distortions, such as the mode-related crosstalk is also introduced when the MDM is applied. In this paper, we propose a semiconductor optical amplifier based optical reservoir (SOA-OR) to perform the signal compensation function in the MDM system. We set up the experimental platform to measure the input-to-output power transferring curve, which was used as the activation function. Moreover, a three-mode (LP01, LP11a and LP11b) point-to-point link with a 12 km few-mode fiber (FMF) was tested. The signal compensation against the mode-related crosstalk was performed by using the proposed SOA-OR scheme. According to the experimental results, a clear signal-quality gain over 8.2 dB was achieved by SOA-OR, which proves the availability of the optical reservoir in MDM transmission systems. © 2024 IEEE.

关键词： Crosstalk

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Vehicular Communication Using NOMA with Antenna Partitioning 15

Vehicular Communication Using NOMA with Antenna Partitioning

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15th International Conference on information and Communication technology Convergence, ICTC 2024

作者： Nduwayezu, Maurice Ofori-Amanfo, Kofi A. Asiedu, Derek K. P. Yun, Ji-Hoon Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul01811 Korea Republic of IMT-Atlantique Lab-STICC Brest29238 France

We solve a sum-rate maximization problem in a downlink vehicular nonorthogonal multiple access (NOMA) system by optimally constructing subarrays and mapping each to a per-vehicle NOMA stream, achieving higher sum-rate... 详细信息

ISBN: (纸本)9798350364637

关键词： Clutter (information theory)

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Use Anchor-Free Based Object Detectors to Detect Surface Defects

Use Anchor-Free Based Object Detectors to Detect Surface Def...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Jiaxue Zhang, Chao Li, Jianjun College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China College of Information Engineering Inner Mongolia University of Science and Technology Baotou014000 China

ISBN: (纸本)9783031702341

Many anchor-based object detectors have limitations in the detection process because of the presence of anchors. Therefore, in recent years, many researchers have shifted their focus to designing anchor-free object detectors. In this paper, we initially employ several common anchor-free detectors (CenterNet, FCOS, YOLOX-S, and YOLOV8-S) to detect surface defects in the hot-rolled strip datasets from Northeastern University (NEU-DET). Following experimental comparisons, YOLOV8-S demonstrates the best detection performance (mAP is 78.04% and the FPS is 8.45). Secondly, we utilize transfer learning to mitigate the overfitting problem that large models can induce on small datasets. Finally, we conduct a large number of experimental comparisons to verify the effectiveness of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Transfer learning

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services 2

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Tesfa, Tarikwa Belete, Befikadu Abera, Samuel Mohapatra, Sudhir Kumar Das, Tapan Kumar Addis Ababa Science and Technology University Department of Software Engineering Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Sri Sri University Faculty of Emerging Technologies Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN. © 2024 IEEE.

关键词： Sentiment analysis

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